Resolving media source detection and simulcast monitoring ambiguities with motion sensor data

ABSTRACT

Example methods disclosed herein to resolve media source detection ambiguities include detecting an ambiguity condition associated with media source detection when monitoring media presented by a media output device in communication with at least two media source devices, and in response to detecting the ambiguity condition, determining a source of the media output from the media output device using motion data reported by accelerometers affixed to remote control devices associated with the at least two media source devices. Example methods disclosed herein to resolve simulcast crediting ambiguities include detecting an ambiguity condition associated with simulcast broadcasting when monitoring media presented by a media output device, and in response to detecting the ambiguity condition, adjusting a time associated with a channel change using motion data reported by an accelerometer affixed to a remote control device associated with the media output device.

RELATED APPLICATION(S)

This patent arises from a continuation of U.S. patent application Ser.No. 15/919,164 (now U.S. Pat. No. ______), which is entitled “RESOLVINGMEDIA SOURCE DETECTION AND SIMULCAST MONITORING AMBIGUITIES WITH MOTIONSENSOR DATA,” and which was filed on Mar. 12, 2018, which claims thebenefit of U.S. Provisional Application Ser. No. 62/506,366, which isentitled “RESOLVING MEDIA SOURCE DETECTION AND SIMULCAST MONITORINGAMBIGUITIES WITH MOTION SENSOR DATA” and which was filed on May 15,2017. Priority to U.S. patent application Ser. No. 15/919,164 and U.S.Provisional Application Ser. No. 62/506,366 is claimed. U.S. patentapplication Ser. No. 15/919,164 and U.S. Provisional Application Ser.No. 62/506,366 are hereby incorporated by reference in their respectiveentireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to media monitoring and, moreparticularly, to resolving media source detection and simulcastmonitoring ambiguities with motion sensor data.

BACKGROUND

Some media monitoring systems employ media source detection to determinewhich of several media sources is providing media being presented by amonitored media device. For example, in a typical home environment, atelevision may be communicatively coupled to several different physicalmedia sources, such as, but not limited to, a set-top box (STB), adigital versatile disk (DVD) player, a Blu-ray disk™ player, a gamingconsole, a computer, etc., capable of providing media for presentationby the television. Furthermore, one or more of the physical mediasources may be able to access different broadcast channels, mediastreams, etc. which may also be considered different virtual mediasources. Accurate detection of the active media source (e.g., physicaland/or virtual) providing the media presented by the monitored mediadevice enables reliable identification of the media and, thus, accuratecrediting of audience exposure to the media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a first example media monitoring systemstructured to use motion sensor data as disclosed herein to resolvesource detection and/or simulcast monitoring ambiguities for anillustrated example media entertainment system in which the mediasources are arranged according to a first example arrangement.

FIG. 2 is a block diagram of a second example media monitoring systemstructured to use motion sensor data as disclosed herein to resolvesource detection and/or simulcast monitoring ambiguities for theillustrated example media entertainment system in which the mediasources are arranged according to a second example arrangement.

FIG. 3 is a block diagram of a third example media monitoring systemstructured to use motion sensor data as disclosed herein to resolvesource detection and/or simulcast monitoring ambiguities for theillustrated example media entertainment system in which the mediasources are arranged according to the first example arrangement.

FIG. 4 illustrates example processing and media crediting decisionsperformed by the example media monitoring systems of FIGS. 1-3 whenmonitored media does not correspond to a simulcast scenario.

FIG. 5 illustrates example processing and media crediting decisionsperformed by the example media monitoring systems of FIGS. 1-3 whenmonitored media corresponds to a simulcast scenario.

FIG. 6 is a block diagram of an example implementation of the meterincluded in the example media monitoring systems of FIGS. 1-3

FIG. 7 is a flowchart representative of example machine readableinstructions that may be executed by the example meter of FIGS. 1-3and/or 6 to resolve media source detection ambiguities using motionsensor data in the example media monitoring systems of FIGS. 1-3.

FIG. 8 is a flowchart representative of example machine readableinstructions that may be executed by the example meter of FIGS. 1-3and/or 6 to resolve simulcast detection ambiguities using motion sensordata in the example media monitoring systems of FIGS. 1-3.

FIG. 9 is a block diagram of an example processor platform structured toexecute the example machine readable instructions of FIGS. 7 and/or 8 toimplement the example meter of the example media monitoring systems ofFIGS. 1-3.

Wherever possible, the same reference numbers will be used throughoutthe drawing(s) and accompanying written description to refer to the sameor like parts, elements, etc.

DETAILED DESCRIPTION

Example methods, apparatus, systems and articles of manufacture (e.g.,physical storage media) to resolve media source detection and simulcastmonitoring ambiguities with motion sensor data are disclosed herein.Some example techniques disclosed herein to resolve media sourcedetection ambiguities with motion sensor data use Bluetoothaccelerometer tags affixed to handheld devices, such as remote controldevices, game controllers, etc., to obtain motion data that can be usedas hints for resolving source detection ambiguities. For example, theaccelerometer hints are used when (1) the monitored audio from a mediadevice outputting a media presentation matches audio detected from twoor more possible sources or (2) the monitored audio from the mediadevice does not match audio detected from any of the possible sources.If either condition (1) or condition (2) above is true, example sourceambiguity resolution techniques disclosed herein use knowledge of thearrangement of the source devices (e.g., whether sources are daisychained or coupled to separate inputs of the media device) and theaccelerometer motion data to determine which source provided the mediabeing output by the monitored media device. In some examples, if neithercondition (1) nor condition (2) above is true, the unique match of themonitored audio from the media device and the audio detected from justone of the possible sources is sufficient to identify the media source.

Some example techniques disclosed herein to resolve simulcast detectionambiguities with motion sensor data use Bluetooth accelerometer tagsaffixed to handheld devices, such as remote control devices, gamecontrollers, etc., to obtain motion data that can be used as channelchange hints for resolving simulcast crediting. In a simulcast scenario,a media device meter employing audio matching (e.g., based onwatermarks, signatures and/or other audio correlation/matchingtechniques) may be unable to detect a channel change between twochannels broadcasting the same simulcast content until the contentchanges. Example simulcast ambiguity resolution techniques disclosedherein use motion data reported by a Bluetooth accelerometer tag affixedto a remote control to detect channel change hints. The channel changehints are used to identify when a channel change occurred to credit thesimulcast broadcast to the proper channel.

By using accelerometer motion data to generate source detection and/orchannel change hints in accordance with the teachings of thisdisclosure, the example source and simulcast ambiguity resolutiontechniques disclosed herein are not limited to a particular type ofremote control technology (e.g., infrared (IR) vs. Bluetooth vs. WiFI)and do not require learning specific device commands or maintaining adatabase of such commands.

These and other example methods, apparatus, systems and articles ofmanufacture (e.g., physical storage media) to resolve media sourcedetection and simulcast monitoring ambiguities with motion sensor dataare disclosed in greater detail below.

Usage of infrared (IR) or radio based (e.g., WiFi, Bluetooth, etc.)remote control devices for controlling audio video (AV) devices can bedetected by capturing infra-red communication or radio signal commands.Some existing media device meters (also referred to as audiencemeasurement meters, site units, home units, etc.) detect the Audio-Video(AV) sources or, more generally, media sources providing media to themedia presentation device (e.g., TV) by correlating audio signals from amicrophone sensing the audio output by the media presentation device(e.g., TV) with audio signals acquired from the sources outputs (e.g.,via audio taps). However, there are some scenarios in which audiocorrelation is not sufficient, is not possible or is otherwise avoided.For example, audio signal acquisition from AV equipment can increasecomplexity and cost of the audience meters and/or overall mediamonitoring system, and can interfere with other devices in the home.Example techniques disclosed herein to resolve media source detectionand simulcast monitoring ambiguities with motion sensor data canovercome some of these limitations of existing meters and monitoringsystems.

FIGS. 1-3 illustrate example media monitoring systems 100, 200 and 300structured to use motion sensor data as disclosed herein to resolvesource detection and/or simulcast monitoring ambiguities when monitoringmedia presented by an example media output device 105 of an illustratedexample media entertainment system 110. As noted above, like/similarelements in the figures are labeled with the same reference numerals. Asused herein, the term “media” includes any type of content and/oradvertisement delivered via any type of distribution medium. Thus, mediaincludes television programming or advertisements, radio programming oradvertisements, movies, web sites, streaming media, etc. Examplemethods, apparatus, and articles of manufacture disclosed herein monitormedia presentations at media devices. Such media devices may include,for example, Internet-enabled televisions, personal computers,Internet-enabled mobile handsets (e.g., a smartphone), video gameconsoles (e.g., Xbox®, PlayStation®), tablet computers (e.g., an iPad®),digital media players (e.g., a Roku® media player, a Slingbox®, etc.),etc. In some examples, media monitoring information is aggregated todetermine ownership and/or usage statistics of media devices, relativerankings of usage and/or ownership of media devices, types of uses ofmedia devices (e.g., whether a device is used for browsing the Internet,streaming media from the Internet, etc.), and/or other types of mediadevice information. In examples disclosed herein, monitoring informationincludes, but is not limited to, media identifying information (e.g.,media-identifying metadata, codes, signatures, watermarks, and/or otherinformation that may be used to identify presented media), applicationusage information (e.g., an identifier of an application, a time and/orduration of use of the application, a rating of the application, etc.),and/or user-identifying information (e.g., demographic information, auser identifier, a panelist identifier, a username, etc.).

The example media entertainment system 110 of FIGS. 1-3 includes twoexample media source devices 115 and 120, also referred to as AV devices115 and 120, which are labeled AV1 and AV2 in the drawings, and theexample media output device 105, also referred to as a mediapresentation device 105, such as a television or any other AVpresentation device, which is labeled TV in the drawings. Therefore, inthe examples of FIGS. 1-3, there are three possible sources of media tobe presented by the media output device (e.g., TV), namely, the mediasource devices 115 and 120 (i.e., AV1 and AV2) or the media outputdevice 105 (e.g., TV) itself. The AV devices 115 and 120 (AV1 and AV2)can be any type of media source device, such as, but not limited to, aset-top box (e.g., cable receiver, satellite receiver, Internet protocoltelevision (IPTV) receiver, etc.), a digital media player (e.g., a Roku®media player, a Slingbox®, a Tivo®, etc.) or other over the top (OTT)device, a DVD player, a VCR, a game console, etc.

The illustrated example media monitoring systems 100 and 200 of FIGS. 1and 2 include an example microphone 125 (labeled MIC) to sense the audiooutput from the media output device 105 (labeled TV), and example audiotaps 130 and 135 (labeled Tap 1 and Tap 2) to sense the audio outputfrom the two media source devices 115 and 120 (AV1 and AV2),respectively. In some examples, the line audio of the media outputdevice 105 (labeled TV) can be sensed, in addition to or as analternative to using the microphone 125. An example meter 140 monitorsthe audio signals sensed by the microphone 125 (MIC) and the audio taps130 and 135 (labeled Tap 1 and Tap 2). As such, the audio taps 130 and135 can be implemented by any device capable of tapping, splitting,etc., the audio output by a media source such that the audio can bereceived/input by two or more other devices. The meter 140 can be anymedia monitoring meter, audience measurement meter, etc., for monitoringmedia presented by a media device, such as the media output device 105.To perform source detection, the meter 140 correlates the audio signalsensed by the microphone 125 (MIC) with the respective audio signalssensed by the audio taps 130 and 135 (labeled Tap 1 and Tap 2) todetermine whether the microphone audio signal matches one or both of theaudio tap signals. A correlation match (e.g., a positive correlationsatisfying a threshold, such as by meeting or exceeding the threshold)means that audio signal captured on the microphone 125 (MIC) correspondsto the audio signal output from the media source devices 115 and/or 120(AV1 and/or AV2). If there is no match, then the media output device 105(e.g., TV), itself is a source of media output signal.

The audio correlation performed by the meter 140 in the illustratedexamples of FIGS. 1 and 2 can involve correlating sampled audio streamsobtained from the microphone 125 (MIC) and from the audio taps 130 and135 (labeled Tap 1 and Tap 2), correlating audio signatures generatedfrom sampled audio streams obtained from the microphone 125 (MIC) andfrom the audio taps 130 and 135 (labeled Tap 1 and Tap 2), correlatingwatermarks detected from sampled audio streams obtained from themicrophone 125 (MIC) and from the audio taps 130 and 135 (labeled Tap 1and Tap 2), etc. Audio watermarking is a technique used to identifymedia such as television broadcasts, radio broadcasts, advertisements(television and/or radio), downloaded media, streaming media,prepackaged media, etc. Existing audio watermarking techniques identifymedia by embedding one or more audio codes (e.g., one or morewatermarks), such as media identifying information and/or an identifierthat may be mapped to media identifying information, into an audioand/or video component. In some examples, the audio or video componentis selected to have a signal characteristic sufficient to hide thewatermark. As used herein, the terms “code” or “watermark” are usedinterchangeably and are defined to mean any identification information(e.g., an identifier) that may be inserted or embedded in the audio orvideo of media (e.g., a program or advertisement) for the purpose ofidentifying the media or for another purpose such as tuning (e.g., apacket identifying header). As used herein “media” refers to audioand/or visual (still or moving) content and/or advertisements. Toidentify watermarked media, the watermark(s) are extracted and used toaccess a table of reference watermarks that are mapped to mediaidentifying information.

Unlike media monitoring techniques based on codes and/or watermarksincluded with and/or embedded in the monitored media, fingerprint orsignature-based media monitoring techniques generally use one or moreinherent characteristics of the monitored media during a monitoring timeinterval to generate a substantially unique proxy for the media. Such aproxy is referred to as a signature or fingerprint, and can take anyform (e.g., a series of digital values, a waveform, etc.) representativeof any aspect(s) of the media signal(s)(e.g., the audio and/or videosignals forming the media presentation being monitored). A signature maybe a series of signatures collected in series over a timer interval. Agood signature is repeatable when processing the same mediapresentation, but is unique relative to other (e.g., different)presentations of other (e.g., different) media. Accordingly, the term“fingerprint” and “signature” are used interchangeably herein and aredefined herein to mean a proxy for identifying media that is generatedfrom one or more inherent characteristics of the media.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media sources. Various comparison criteria, such as a cross-correlationvalue, a Hamming distance, etc., can be evaluated to determine whether amonitored signature matches a particular reference signature. When amatch between the monitored signature and one of the referencesignatures is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that with matched the monitored signature. Becauseattributes, such as an identifier of the media, a presentation time, abroadcast channel, etc., are collected for the reference signature,these attributes may then be associated with the monitored media whosemonitored signature matched the reference signature. Example systems foridentifying media based on codes and/or signatures are long known andwere first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is herebyincorporated by reference in its entirety.

The illustrated examples of FIGS. 1 and 2 depict different sourcedetection ambiguities that can arise when using only audio correlation(e.g., stream matching, signature matching, watermark matching, etc.)for media source detection. In both examples, under some circumstances,the meter 140 may determine that the audio signals sensed by both of theaudio taps 130 and 135 (Tap 1 and Tap 2), which correspond to therespective media sources 115 and 120 (AV1 and AV2), match (e.g., haverespective positive correlations satisfying a threshold, such as bymeeting or exceeding the threshold) the audio signal captured on themicrophone 125 (MIC), which corresponds to output of the media outputdevice 105 (e.g., TV). In the illustrated example of FIG. 1, such amedia source detection ambiguity can arise due to source simulcasting,in which both source devices 115 and 120 (AV1 and AV2) receive the samemedia content through different feeds (e.g., labeled as Feed1, Feed2)because both sources 115 and 120 are tuned to the same broadcaster, orto two different broadcasters generating the same content for the giventime period. The feeds in the illustrated examples can be any typeand/or number of media feeds, such as, but not limited to, a terrestrialTV feed, a cable feed, a satellite feed, an IPTV feed, an Internet feed,etc. In the illustrated example of FIG. 2, such a media source detectionambiguity can arise due to the daisy-chain arrangement of the mediasource devices 115 and 120 (AV1 and AV2), in which the media sourcedevice 120 (AV2) is connected as an input to the media source device 115(AV1), and the media source device 115 (AV1) can pass-through the signalreceived from the media source device 120 (AV2) and output this signalto the media output device 105 (e.g., TV).

The example media monitoring system 300 of FIG. 3 illustrates an exampleimplementation in which audio sensing and correlation is unavailable orotherwise not implemented for media source detection. As such, in theillustrated example of FIG. 3, media source detection ambiguities canarise because monitored audio signals for source detection are notavailable, or are avoided because of complexity, user acceptance and/orhigh costs.

To help resolve such media source detection ambiguities, some priormedia monitoring systems capture infrared (IR) commands from remotecontrols as hints to relay to the meter and back office when audiocorrelations are not sufficient for source detection. However, newerremote controls, as well also game pads, can use radio frequencycommunication, such as Bluetooth Low Energy (BLE) wireless radiocommunication, to communicate with a media (e.g., AV) source deviceand/or media output device (e.g., TV). Capturing (e.g., sniffing) suchwireless radio communication can be a complex task and sometimes is notfeasible. For example, the BLE access address of the remote controldevice may need to be known before BLE packets can be captured tounderstand the activity of the wireless radio remote control.

In contrast, example source and simulcast ambiguity resolutiontechniques disclosed herein can be used in the example media monitoringsystems 100, 200 and 300 of FIGS. 1, 2 and/or 3 to ascertain sourcedetection hints from remote control usage regardless of the type ofremote control. Remote control devices for media (e.g., AV) sourcedevices and media output devices (e.g., TVs) are generally hand-helddevices. When used, a remote control device (also referred to herein asa remote control) is held in the hand of a user (e.g., viewer, listener,etc.) and, therefore, motion can be an indication of use. Example sourceand simulcast ambiguity resolution techniques disclosed herein use tagshaving accelerometers supporting BLE communication to detect motion ofremote control devices. Such BLE accelerometer tags can be sufficientlysmall to be affixed to remote controls, game pads, etc., and have arelatively long battery lifetime (e.g., 1 year or more). By using suchBLE accelerometer tags, the detected motion data can replace, or atleast augment, the need for infra-red and/or radio based capturingtechnology for providing source detection and channel change hints to TVaudience meters, such as the example meter 140 in the example mediamonitoring systems 100, 200 and 300 of FIGS. 1-3.

For example, the example media entertainment system 110 in theillustrated examples of FIGS. 1-3 includes an example remote control 145(labeled TV Remote Control) to control the media output device 105(e.g., TV), an example remote control device 150 (labeled AV1 RemoteControl) to control the media source device 115 (e.g., AV1 Device) andan example remote control device 155 to control the media source device120 (e.g., AV2 Device). In the illustrated examples of FIGS. 1-3, themedia monitoring systems 100, 200 and 300 include an example BLEaccelerometer tag 160 (labeled Accelerometer TV) affixed or otherwisecoupled to the remote control 145 to sense motion associated withcontrolling the media output device 105 (e.g., TV) and to transmit themotion activity data (e.g., labeled as BLE Comm.) to an example BLEreceiver 165 included in or in communication with the example meter 140.The example media monitoring systems 100, 200 and 300 also include anexample BLE accelerometer tag 170 (labeled Accelerometer AV1) affixed orotherwise coupled to the remote control 150 to sense motion associatedwith controlling the media source device 115 (e.g., AV1) and to transmitthe motion activity data (e.g., labeled as BLE Comm.) to the example BLEreceiver 165. The example media monitoring systems 100, 200 and 300further include an example BLE accelerometer tag 175 (labeledAccelerometer AV2) affixed or otherwise coupled to the remote control155 to sense motion associated with controlling the media source device120 (e.g., AV2) and to transmit the motion activity data (e.g., labeledas BLE Comm.) to the example BLE receiver 165. As such, the BLE receiver165 can be implemented by any BLE receiver capable of receiving BLEpackets advertised or otherwise transmitted by BLE accelerometer tags.

In some examples, additional accuracy can be obtained by also using amagnetometer and/or gyroscope, but this may result in higher powerconsumption. Thus, a given example implementation of the source andsimulcast ambiguity resolution techniques disclosed herein may use oromit additional magnetometer and/or gyroscope functionality based on atradeoff between accuracy vs. power consumption, tag dimensions, batterylife, etc.

At least the following two types of motion hint data can be obtained byan example meter, such as the meter 140 in the examples of FIGS. 1-3,employing BLE accelerometer tags, such as the BLE accelerometer tags160, 170, 175, with example source and simulcast ambiguity resolutiontechniques disclosed herein. For example, the meter 140 can obtainaverage, or aggregate, motion data from a BLE accelerometer tag (e.g.,one or more of the BLE accelerometer tags 160, 170, 175) affixed to agiven remote control device (e.g., one or more of the remote controldevices 145, 150, 155), which indicates that the given remote controldevice (e.g., one or more of the remote control devices 145, 150, 155)is in use. Additionally or alternatively, the meter 140 can obtainmotion data from a BLE accelerometer tag (e.g., one or more of the BLEaccelerometer tags 160, 170, 175) affixed to a given remote controldevice (e.g., one or more of the remote control devices 145, 150, 155),which is indicative of specific movement(s) of the given remote controldevice (e.g., one or more of the remote control devices 145, 150, 155),such as gestures (ascertained by evaluating maximal values, average,values, a certain sequence of events, etc., and/or a combinationthereof), a possibility that a given key has pressed on the remotecontrol (and, thus, that the remote control is actively used), etc. Suchmotion hint data can be used to resolve the source detection ambiguitiesthat can arise in prior media monitoring systems when using only audiocorrelation for media source detection.

For example, FIG. 1 illustrates an example simulcast scenario in whichboth media source devices 115 and 120 (e.g., AV1 and AV2) can be tunedto the same channel, or to different channels broadcasting the samemedia content, and, thus, receive the same source media from inputsFeed1 and Feed2. FIG. 2 illustrates an example daisy-chain scenario inwhich the AV signal from the media source devices 120 (AV2) can passthrough the media source devices 115 (AV1). In both example scenarios,the same audio signal is output from both of the media source devices115 and 120 (AV1 and AV2). As disclosed in further detail below, remotecontrol motion hint data can be used to resolve the potential ambiguityas to which media source 115 or 120 is providing the media being outputby the media output device 105. Remote control motion hint data can alsobe used to resolve the source detection ambiguities that arise when noaudio correlation data is available for media source detection, such asin the example of FIG. 3. An example program than can be executed by theexample meter 140 to leverage remote control motion hint data to resolvemedia source detection ambiguities is illustrated FIG. 7, which isdescribed in further detail below.

The example media monitoring systems 100, 200 and 300 of FIGS. 1-3 alsoinclude an example back office 180 associated with an audiencemeasurement entity (AME) (e.g., such as The Nielsen Company (US), LLC.)The back office 180 can be implemented by, for example, a centraloffice, central processing facility including and/or accessing one ormore servers, processing clouds, etc. In the illustrated examples, theback office 180 is in communication with the meter 140 via an examplecommunication channel 185 (labeled Comm. Channel), which can beimplemented by any communication network (e.g., private/public),connection, link, etc. In the illustrated example, the back office 180receives meter data reported by the meter 120, which can include mediaidentification data (e.g., watermarks, signatures, etc.) identifying themedia presented by the media output device 105 (e.g., TV), as well asdata identifying the source(s) of presented media, which sourceambiguities being resolved by the meter 140 using motion hint data inaccordance with the teachings of this disclosure. In some examples, themeter 140 reports the motion hint data to the back office 180, whichimplements the example program of FIG. 7 to resolve media sourcedetection ambiguities.

FIGS. 4-5 illustrate operation of the example media monitoring systems100, 200, 300 of FIGS. 1-3 to resolve simulcast monitoring ambiguitieswhen monitoring media presented by the illustrated example mediaentertainment system 110. In the illustrated examples of FIGS. 4-5, barslabeled B1 represent media (e.g., content, advertisements, etc.)broadcast by a first broadcaster (e.g., broadcaster B1), whereas barslabeled B2 represent media (e.g., content, advertisements, etc.)broadcast by a second broadcaster (e.g., broadcaster B2). In theillustrated examples of FIGS. 4-5, bars labeled PANELIST represent theground truth corresponding to what media the panelist actually consumed,whereas bars labeled AUDIO MATCHER represent the media that anaudio-based media matching system not employing motion sensor hints asdisclosed herein would determine that the panelist consumed. In theillustrated example of FIG. 5, the bar labeled AUDIO MATCHER WITH MOTIONSENSOR HINTS represents the media that an audio-based media matchingsystem that does employ motion sensor hints as disclosed herein (e.g.,one or more of the example media monitoring systems 100, 200, 300) woulddetermine that the panelist consumed.

As noted above, audio matching (e.g., signature-based, watermark-based,etc.) for media crediting includes at least two parts. A first partinvolves maintaining (e.g., stored in the back office) reference audiodata (e.g., reference signatures, reference watermarks, etc.) for thebroadcast channels of interest to be credited. A second part involvesobtaining monitored audio data (e.g., monitored signatures, monitoredwatermarks, etc.) coming from the panelist metering equipment andrepresenting what panelist has watched. The process of crediting mediaconsumption includes looking for parts of audio in reference tracks thatmatch parts of audio in the monitored audio coming from panelistmetering equipment.

In the illustrated example of FIG. 4, there are two broadcasters, B1 andB2. In a typical broadcast scenario, the broadcasters B1 and B2 arebroadcasting different content and, thus, they are broadcastingdifferent audio. In such a scenario, a media monitoring system employingaudio matching can determine that a panelist switched from B1 to B2 attime 400 by comparing the monitored audio to the reference audio, whichis sufficient to identify what the panelist was watching and when thepanelist switched from B1 to B2.

The example of FIG. 5 illustrates limitations of audio matching withoutthe use of motion hints as disclosed herein. One such limitation is theinability to differentiate between two broadcasters when they broadcastthe same content, referred to as simulcast broadcasting. For example, inFIG. 5, broadcasters B1 and B2 broadcast the same program for the firstcouple of hours (e.g., up to time 500), with the only difference in themedia being broadcast during this time being a short commercial break505 that B1 inserted at time 510.

Some prior media monitoring systems attempt to resolve simulcastcrediting in two parts. First, the prior systems match audio blocks(corresponding to the bar labeled AUDIO MATCHER). With reference to theexample of FIG. 5, initially, the reference audio coming from bothbroadcasters is the same. The prior audio matching algorithm has twopotential match candidates and, in the illustrated example, randomlyselects B2 (see reference numeral 515). In this example, the selectionof B2 happens to be correct. However, later on, the crediting of B2becomes wrong (see reference numeral 520), since the prior audio matcher(corresponding to the bar labeled AUDIO MATCHER) has credited part ofthe panelist's viewing of broadcaster B l's broadcast to broadcaster B2.In the illustrated example, such attribution persists until the prioraudio matcher reaches the commercial insertion (at time 510). Untilthen, the prior audio matching algorithm has no reason to believe thepanelist switched sources. At the commercial insertion point (time 51),the audio is different and the prior audio matcher (corresponding to thebar labeled AUDIO MATCHER) then correctly switches crediting tobroadcaster B1 (see reference number 525). In the illustrated example,the panelist later switches back to B2 and again the prior audio matcheralgorithm (corresponding to the bar labeled AUDIO MATCHER) makes anerror by wrongly attributing part of the panelist's viewing of the B2broadcast to B1 (see reference numeral 530). Such incorrect creditingpersists until the simulcast is over and the reference audio becomesdifferent, at which point the audio matcher can correctly attributeviewing (see reference numeral 535).

To resolve the simulcast crediting errors associated with the using justan audio matching algorithm (corresponding to the bar labeled AUDIOMATCHER), some prior media monitoring systems may use panelist IR remotecontrol stream detection to try to detect the time of a channel change.For example, some such prior media monitoring systems detect IR activityfrom the TV remote control when the panelist changes programs, and storetimestamps of IR events. The prior audio matcher can use the timestampsof IR activity to backdate the channel switch to a point earlier thanthe commercial insertion point, which can allow the viewing to becorrectly attributed to the broadcaster. In general, prior mediamonitoring system can user IR remote control activity to backdatechannel switches detected by the audio matcher to earlier points intime.

However, such prior techniques based on IR remote control activity havelimitations. Detecting/listening to IR remote control stream typicallyrequires a process of teaching the metering device to recognize IRactivity of a particular consumer device. This extends time to installthe meter, and requires database storage for IR data library in themeter and/or in the back office and continuous maintenance of thedatabase with new consumer devices. Also, IR remote control technologyis becoming outdated and many consumer device vendors are replacing IRtechnology with Bluetooth and/or WiFi remote controls. However,detecting/sniffing Bluetooth and/or WiFi communications can bedifficult, if not impossible, because such communications are oftenencrypted. This makes it difficult to use remote control activity hintswhen resolving simulcast crediting, which can degrade the accuracycrediting with audio matching technology.

In contrast, example source and simulcast ambiguity resolutiontechniques disclosed herein use tags (e.g., such as the tags 160, 170,175) having accelerometers with BLE communication to detect channelchange hints from operation of remote control devices (e.g., such as theremote control devices 145, 150, 155). By attaching a battery poweredwireless accelerometer sensor to a remote control device (e.g., as shownin the examples of FIGS. 1-3), the example media monitoring systems 100.200. 300 employing source and simulcast ambiguity resolution techniquesdisclosed herein can determine when the panelist has been holding agiven remote control in his/her hand. Such disclosed media monitoringsystems can translate the motion data obtained from the accelerometertags to a channel change and/or other remote control activity hint thatcan be used to backdate channel switching. Such back-dating isillustrated by reference numerals 540 and 545 associated with the barlabeled AUDIO MATCHER WITH MOTION SENSOR HINTS of the example of FIG. 5.In general, by processing the motion sensor data from an accelerometertag affixed to a remote control device, disclosed example source andsimulcast ambiguity resolution techniques can determine what thepanelist has been trying to control and, thus, deduce the potentialsource of the content.

Example source and simulcast ambiguity resolution techniques disclosedherein have many benefits. For example, such techniques do not requiretraining the system to learn the remote control commands. Thus,installation time is shorter. Also, because there is no need fortraining, there is also no need for storage space in the back office tostore IR library data and also no need for maintenance if it. Further,such disclosed techniques can monitor all known, and future, types ofremote controls. There is no need to update tracking technology when anew remote control technology appears on the field. This is becausemonitoring relies on a battery powered accelerometer affixed to the userremote control and does not rely on the particular technology employedby the remote control to communicated with the target device.

In some examples, further hints on what remote control command wasactivated by the user (e.g., channel change, volume up, . . . ) can beachieved by analyzing motion data coming from the accelerometer tag. Forexample, motion data associated with a long key press (e.g.,corresponding to a volume up/down command) can be distinguished frommotion data associated with a short key press (e.g., corresponding to achannel change command).

An example program than can be executed by the example meter 140 and/orthe example back office 180 to leverage remote control motion hint datato resolve simulcast crediting ambiguities is illustrated FIG. 8, whichis described in further detail below.

A block diagram of an example implementation of the meter 140 includedin the example media monitoring systems 100, 200, 300 of FIGS. 1-3 isillustrated in FIG. 6. The example meter 140 of FIG. 6 includes anexample audio signal monitor 605 to receive and process, as describedabove and in further detail below, the audio signals obtained by theexample microphone 125 and the example audio taps 130 and 135 todetermine media source ambiguity conditions, media monitoring data(e.g., based on watermarks, signatures, etc.), etc. The example meter140 of FIG. 6 also includes an example media source resolver 610 toprocess remote control motion hint data received by the example BLEreceiver 165 from one or more of the example accelerometer tags 160,170, 175 to resolve, as described above and in further detail below,media source detection ambiguities identified by the audio signalmonitor 605. The example meter 140 of FIG. 6 also includes an examplesimulcast resolver 615 to process the remote control motion hint datareceived by the example BLE receiver 165 from one or more of the exampleaccelerometer tags 160, 170, 175 to resolve, as described above and infurther detail below, simulcast crediting ambiguities identified by theaudio signal monitor 605. The example meter 140 of FIG. 6 furtherincludes an example metering data reporter 620 to report metering dataincluding the media identification data determined by the audio signalmonitor 605 and the media source identification data determined by theexample media source resolver 610 and/or the example simulcast resolver615 to the back office 180. Example programs that may be executed toimplement the example meter 140 of FIG. 6 are illustrated in FIGS. 7-8,which are described in further detail below.

While example manners of implementing example media monitoring systems100, 200, and/or 300 in accordance with the teachings of this disclosureare illustrated in FIGS. 1-6, one or more of the elements, processesand/or devices illustrated in FIGS. 1-6 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example meter 140, the example BLE receiver 165, theexample BLE accelerometer tags 160, 170 and/or 175, the example backoffice 180, the example audio signal monitor 605, the example mediasource resolver 610, the example simulcast resolver 615, the examplemetering data reporter 620 and/or, more generally, the example mediamonitoring systems 100, 200 and/or 300 of FIGS. 1-6 may be implementedby hardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example meter140, the example BLE receiver 165, the example BLE accelerometer tags160, 170 and/or 175, the example back office 180, the example audiosignal monitor 605, the example media source resolver 610, the examplesimulcast resolver 615, the example metering data reporter 620 and/or,more generally, the example media monitoring systems 100, 200 and/or 300of FIGS. 1-6 could be implemented by one or more analog or digitalcircuit(s), logic circuits, programmable processor(s), applicationspecific integrated circuit(s) (ASIC(s)), programmable logic device(s)(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). Whenreading any of the apparatus or system claims of this patent to cover apurely software and/or firmware implementation, at least one of theexample meter 140, the example BLE receiver 165, the example BLEaccelerometer tags 160, 170 and/or 175, the example back office 180, theexample audio signal monitor 605, the example media source resolver 610,the example simulcast resolver 615, the example metering data reporter620 and/or, more generally, the example media monitoring systems 100,200 and/or 300 of FIGS. 1-6 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample media monitoring systems 100, 200 and/or 300 of FIGS. 1-6 mayinclude one or more elements, processes and/or devices in addition to,or instead of, those illustrated in FIGS. 1-3, and/or may include morethan one of any or all of the illustrated elements, processes anddevices.

Flowcharts representative of example machine readable instructions forimplementing the example meter 140 and/or, more generally, the examplemedia monitoring systems 100, 200 and/or 300 of FIGS. 1-6 are shown inFIGS. 7-8. In these examples, the machine readable instructions compriseone or more programs for execution by a processor, such as the processor912 shown in the example processor platform 900 discussed below inconnection with FIG. 9. The one or more programs, or portion(s) thereof,may be embodied in software stored on a non-transitory computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a Blu-ray disk™, or a memory associated with theprocessor 912, but the entire program or programs and/or parts thereofcould alternatively be executed by a device other than the processor 912and/or embodied in firmware or dedicated hardware (e.g., implemented byan ASIC, a PLD, an FPLD, discrete logic, etc.). Further, although theexample program(s) is(are) described with reference to the flowchartsillustrated in FIGS. 7-8, many other methods of implementing the examplemeter 140 and/or, more generally, the example media monitoring systems100, 200 and/or 300 of FIGS. 1-6 may alternatively be used. For example,with reference to the flowcharts illustrated in FIGS. 7-8, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, combined and/or subdivided intomultiple blocks. Additionally or alternatively, any or all of the blocksmay be implemented by one or more hardware circuits (e.g., discreteand/or integrated analog and/or digital circuitry, a Field ProgrammableGate Array (FPGA), an Application Specific Integrated circuit (ASIC), acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

As mentioned above, the example processes of FIGS. 7-8 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim lists anythingfollowing any form of “include” or “comprise” (e.g., comprises,includes, comprising, including, etc.), it is to be understood thatadditional elements, terms, etc. may be present without falling outsidethe scope of the corresponding claim. As used herein, when the phrase“at least” is used as the transition term in a preamble of a claim, itis open-ended in the same manner as the term “comprising” and“including” are open ended. Also, as used herein, the terms “computerreadable” and “machine readable” are considered equivalent unlessindicated otherwise.

An example program 700 that may be executed in the example meter 140 ofthe example media monitoring systems 100, 200 and/or 300 of FIGS. 1-6 toleverage remote control motion hint data to resolve media sourcedetection ambiguities is illustrated FIG. 7. In the illustrated exampleof FIG. 7, a positive audio correlation match between the audio signalcaptured on the microphone 125 (MIC), which corresponds to the output ofthe media output device 105 (e.g., TV), and the audio signal sensed byone of the audio taps 130 and 135 (e.g., Tap 1 and Tap 2), whichcorrespond to the respective media sources 115 and 120 (e.g., AV1 orAV2) is represented by the value 1, whereas the value 0 represents nocorrelation match. Thus, in the example of FIG. 7, the notation(MIC<->AV1)=1 corresponds to the scenario in which the sensed audio fromthe microphone 125 (MIC) and the sensed audio from the tap 130corresponding to the media source 115 (AV1) have a correlation match,and the notation (MIC<->AV1)=0 corresponds to the scenario in which thesensed audio from the microphone 125 (MIC) and the sensed audio from thetap 130 corresponding to the media source 115 (AB1) do not have acorrelation match. Likewise, in the example of FIG. 7, the notation(MIC<->AV2)=1 corresponds to the scenario in which the sensed audio fromthe microphone 125 (MIC) and the sensed audio from the tap 135corresponding to the media source 120 (AV2) have a correlation match,and the notation (MIC<->AV2)=0 corresponds to the scenario in which thesensed audio from the microphone 125 (MIC) and the sensed audio from thetap 135 corresponding to the media source 120 (AV2) do not have acorrelation match. Also, in the example of FIG. 7, the symbol “∧”represents the logical AND operation, and the symbol “∨” represents thelogical OR operation.

As illustrated in FIG. 7, the example program 700 begins with the audiosignal monitor 605 of the meter 140 comparing the sensed audio from themicrophone 125 (MIC) with the sensed audio from the audio taps 130 and135 (e.g., Tap 1 and Tap 2), which correspond to the respective mediasources 115 and 120 (e.g., AV1 or AV2). (Block 705). The example mediasource resolver 610 of the meter 140 also monitors the remote controlmotion data reported by one or more of the BLE accelerometer tags 160,170, 175 affixed to the respective remote control devices 145, 150, 155to identify a winning accelerometer, which may correspond to theaccelerometer and, thus, remote control device most recently used (e.g.,most recently exhibiting motion), the accelerometer associated with aparticular type of motion (e.g., a channel change performed by theassociated remote control device), etc. (See block 710). The examplemedia source resolver 610 further employs such remote control motionhint data to resolve source detection ambiguities in response todetecting an ambiguity condition associated with media source detection.

For example, the media source resolver 610 employs remote control motionhint data to resolve source detection ambiguities when the audio signalmonitor 605 of the meter 140 determines the sensed audio from themicrophone 125 (MIC), which corresponds to the output of the mediaoutput device 105 (e.g., TV), matches sensed audio from multiple mediasources 115 and 120 (e.g., AV1 and AV2), such as when(MIC<->AV1)=1∧(MIC<->AV2)=1, which is a first example of an ambiguitycondition associated with media source detection that can be identifiedby the audio signal monitor 605. (See Block 715). As shown in FIG. 7,when such a condition is satisfied, the example media source resolver610 of the meter 140 uses motion detection hints, such as finding awinning accelerometer corresponding to the accelerometer reporting thelast (e.g., most recent) motion data representative of a motion activitylevel, or in other words, an amount of movement satisfying a movementthreshold and considering also any special movements detection (e.g.,gestures, command inputs, etc.). For example, if(MIC<->AV1)=1∧(MIC<->AV2)=1 (corresponding to “Yes” out of block 715 and“No” out of block 720), and the media sources 115 and 120 (e.g., AV1 andAV2) are not arranged in a daisy chain (e.g., as specified byconfiguration data provided to the program 700) (corresponding to “No”out of block 725), the example media source resolver 610 of the meter140 detects the active media source to be the source associated with theremote control device coupled to the winning BLE accelerometer tag(e.g., the tag associated with the most recent motion, highest level ofmotion, most relevant motion, such as motion associated with input of aspecific command, etc., as determined at block 710) (see block 730).However, if (MIC<->AV1)=1∧(MIC<->AV2)=1 (corresponding to “Yes” out ofblock 715 and “No” out of block 720), and the media sources 115 and 120(e.g., AV1 and AV2) are arranged in a daisy chain (e.g., as specified byconfiguration data provided to the program 600) corresponding to “Yes”out of block 725), the example media source resolver 610 of the meter140 determines if the winning BLE accelerometer tag is associated withthe remote control 160 of the media output device 105 (e.g., the TVremote control). (Block 735). If the winning BLE accelerometer tag isnot associated with the TV remote control 160 (block 735), the winningBLE accelerometer tag is associated with one of the media source devices115 or 120 (e.g., AV1 or AV2) and, thus, the example media sourceresolver 610 of the meter 140 detects the active media source to be thesource associated with the winning BLE accelerometer tag. (Block 740).However, if the winning BLE accelerometer tag is associated with theremote control 160 of the media output device 105 (e.g., the TV remotecontrol), the example media source resolver 610 of the meter 140 assumesthe source is unchanged and, thus, credits the last active source (e.g.,AV1 or AV2) as the currently active media source. (Block 745).

As illustrated in the example program 700 of FIG. 7, the example mediasource resolver 610 of the meter 140 also employs remote control motionhint data to resolve source detection ambiguities when the audio signalmonitor 605 of the meter 140 determines the sensed audio from themicrophone 125 (MIC), which corresponds to the output of the mediaoutput device 105 (e.g., TV), does not match sensed audio from any ofthe multiple media sources 115 and 120 (e.g., AV1 and AV2), such as when(MIC<->AV1)=0∧(MIC<->AV2)=0, which is a second example of an ambiguitycondition associated with media source detection that can be identifiedby the audio signal monitor 605. (See Block 715). As shown in FIG. 7,when such a condition is satisfied (corresponding to “Yes” out of block715 and “Yes” out of block 720), the example media source resolver 610of the meter 140 detects the media output device 105 (e.g., TV) as theactive media source. (Block 750).

As illustrated in the example of FIG. 7, the audio signal monitor 605 ofthe meter 140 may determine that no ambiguity condition associated withmedia source detection exists, such as when the sensed audio from themicrophone 125 (MIC), which corresponds to the output of the mediaoutput device 105 (e.g., TV), matches sensed audio from just one of themultiple media sources 115 and 120 (e.g., AV1 and AV2), such as when(MIC<->AV1)=1∧(MIC<->AV2)=0, or (MIC<->AV1)=0∧(MIC<->AV2)=1. In such acase (corresponding to “No” out of block 715), the example media sourceresolver 610 of the meter 140 assumes the active media source is thesource having the audio that matched the sensed audio from themicrophone 125 (MIC). (Block 755).

As illustrated in the example of FIG. 7, the example media sourceresolver 610 of the meter 140 can also use the remote control motionhint data provided by the BLE accelerometers to identify anchor timecorresponding to when a channel was changed, when a source was changed,when volume was changed, etc. (See blocks 745, 750 and 755).

The example program 700 can be executed on the meter 140 and/or at theback office 180 in the example media monitoring systems 100, 200 and/or300 of FIGS. 1-6. In some examples in which the program 700 is executedprimarily (or entirely) by the meter 140, the meter 140 uses the audiocorrelation result data (e.g., based on comparing audio samples,signatures, watermarks, etc. from the sensed audio from the media outputdevice and the multiple media sources 115 and 120) and motion data fromthe BLE accelerometers 160, 170, 175 according to the example program700 to identify the source (e.g., AV1, AV2 or TV) of the media presentedby the media output device 105 (e.g., TV). In some examples in which atleast relevant portions of the program 700 are executed by the backoffice 180, both audio correlation input data (e.g., corresponding toaudio samples, signatures, watermarks, etc. obtained from the sensedaudio from the media output device and the multiple media sources 115and 120) as determined by the meter 140 and motion data from the BLEaccelerometers 160, 170, 175 can be transferred to the back office 180over the communication channel 185, such as the Internet, as shown inthe examples of FIGS. 1-3. In some such examples, the back office 180uses the audio correlation input data reported by the meter 140 and themotion data from the BLE accelerometers 160, 170, 175 according to theexample program 700 to identify the source (e.g., AV1, AV2 or TV) of themedia presented by the media output device 105 (e.g., TV). In someexamples, the back office 180 also identifies the media being presentedby the media output device (e.g., TV) using signatures and/or watermarksreported by the meter 140.

An example program 800 that may be executed in the example meter 140 ofthe example media monitoring systems 100, 200 and/or 300 of FIGS. 1-3 toleverage remote control motion hint data to resolve media sourcedetection ambiguities is illustrated FIG. 8. As illustrated in theexample program 800 of FIG. 8, the example simulcast resolver 615 of themeter 140 employs remote control motion hint data to resolve simulcastcrediting ambiguities in response to the audio signal monitor 605 of themeter 140 detecting an ambiguity condition associated with simulcastbroadcasting. For example, the example program 800 begins with theexample simulcast resolver 615 of the meter 140 monitoring the remotecontrol motion data reported by one or more of the BLE accelerometertags 160, 170, 175 affixed to the respective remote control devices 145,150, 155. (Block 805). The audio signal monitor 605 of the meter 140also processes the audio signal captured on the microphone 125 (MIC),which corresponds to the output of the media output device 105 (e.g.,TV), to perform media matching/identification (e.g., using watermarks,signatures, etc.). (Block 810). The simulcast resolver 615 then employs,as needed, remote control motion hint data to resolve simulcastcrediting ambiguities when the audio signal monitor 605 of the meter 140detects a channel change from prior matched media to new matched media(e.g., the audio signal monitor 605 detects the media output by themedia output device 105 has changed).

For example, if a channel change is detected by the audio signal monitor605 (e.g., based on identifying a media changes using watermarks,signatures, etc.) (block 815), the audio signal monitor 605 determineswhether the prior matched media corresponds to a simulcast ambiguitycondition (block 820). For example, the audio signal monitor 605 maydetermine the prior matched media corresponds to a simulcast ambiguitycondition if the back office 180 of the media monitoring system 100,200, 300 has reference media data (e.g., signatures watermarks, etc.)for at least two different broadcast sources that match the media (e.g.,audio) sensed from the output of the media output device 105 during thatsame broadcast time. As shown in the example of FIG. 8, if the audiosignal monitor 605 of the meter 140 determines the prior matched mediacorresponds to a simulcast ambiguity condition (block 820), thesimulcast resolver 615 of the meter 140 processes motion data reportedfrom the accelerometer tag 160 affixed to the remote control device 145associated with the media output device 105 to determine channel changehint data (block 825). The simulcast resolver 615 of the meter 140 thenuses this channel change hint data to adjust (e.g., back-date) the timeassociated with the channel change from the prior matched media to newmatched media as detected by the audio signal monitor 605. (Block 830).However, if the audio signal monitor 605 of the meter 140 determines theprior matched media does not correspond to a simulcast ambiguitycondition (block 820), simulcast resolver 615 of the meter 140 does notperform any adjusting (e.g., back-dating) of the time associated withthe channel change (block 835).

The example program 800 can be executed on the meter 140 and/or at theback office 180 in the example media monitoring systems 100, 200, 300 ofFIGS. 1-3. In some examples in which the program 800 is executed by theback office 180, both audio monitoring data determined by the meter 140and motion data from the BLE accelerometers 160, 170, 175 can betransferred from the meter 140 to the back office 180 over thecommunication channel 185, such as the Internet, as shown in FIGS. 1-3.In some such examples, the back office 180 uses the audio monitoringdata from the meter 140 and the motion data from the BLE accelerometers160, 170, 175 to perform audio matching and resolve simulcastambiguities according to the example program 800.

FIG. 9 is a block diagram of an example processor platform 900structured to execute the instructions of FIGS. 7-8 to implement theexample meter 140 and/or, more generally, the example media monitoringsystems 100, 200, 300 of FIGS. 1-3. In some examples, the processorplatform 900 can be used to implement the functionality (e.g.,corresponding to blocks 605, 610 and/or 615 above) of the meter 140 thatis directed to resolving media source detection and simulcast monitoringambiguities in the back office 180. The processor platform 900 can be,for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box a digital camera, or any othertype of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer. The hardware processor 912 may be asemiconductor based (e.g., silicon based) device. In this example, theprocessor 912 implements the example audio signal monitor 605, theexample media source resolver 610, the example simulcast resolver 615and/or the example metering data reporter 620 of the example meter 140included in the example media monitoring systems 100, 200, 300 of FIGS.1-3.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a link 918. The link 918 may be implementedby a bus, one or more point-to-point connections, etc., or a combinationthereof. The volatile memory 914 may be implemented by SynchronousDynamic Random Access Memory (SDRAM), Dynamic Random Access Memory(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any othertype of random access memory device. The non-volatile memory 916 may beimplemented by flash memory and/or any other desired type of memorydevice. Access to the main memory 914, 916 is controlled by a memorycontroller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, a trackbar (such as an isopoint), a voicerecognition system and/or any other human-machine interface. Also, manysystems, such as the processor platform 900, can allow the user tocontrol the computer system and provide data to the computer usingphysical gestures, such as, but not limited to, hand or body movements,facial expressions, and face recognition.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 920 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAID(redundant array of independent disks) systems, and digital versatiledisk (DVD) drives.

Coded instructions 932 corresponding to the instructions of FIGS. 6-7may be stored in the mass storage device 928, in the volatile memory914, in the non-volatile memory 916, in the local memory 913 and/or on aremovable tangible computer readable storage medium, such as a CD or DVD936.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1. (canceled)
 2. An apparatus to perform media source detection, theapparatus comprising: memory including computer readable instructions;and a processor to execute the instructions to at least: determine thatan output media signal from a monitored media device corresponds to (i)a first media signal from a first source device in communication withthe monitored media device, and (ii) a second media signal from a secondsource device in communication with the monitored media device; accessmotion data reported by one or more of a plurality of accelerometersincluding a first accelerometer associated with a first remote controldevice and a second accelerometer associated with a second remotecontrol device, the first remote control device associated with thefirst source device, the second remote control device associated withthe second source device; and identify, based on the motion data, one ofthe first source device or the second source device to be a source ofthe output media signal from the monitored media device.
 3. Theapparatus of claim 2, wherein the processor is to: correlate the outputmedia signal with the first media signal to determine a correlationresult; and determine the output media signal corresponds to the firstmedia signal when the correlation result satisfies a threshold.
 4. Theapparatus of claim 2, wherein the first source device is incommunication with a first media input of the monitored media device,the second source device is in communication with a second media inputof the monitored media device, the motion data includes first motiondata reported by the first remote control device and second motion datareported by the second remote control device, and the processor is tocompare the first motion data and the second motion data to determinewhether the first source device or the second source device is thesource of the output media signal.
 5. The apparatus of claim 4, whereinthe processor is to: identify the first source device to be the sourceof the output media signal when the first motion data is more recentthan the second motion data; and identify the second source device to bethe source of the output media signal when the second motion data ismore recent than the first motion data.
 6. The apparatus of claim 4,wherein to compare the first motion data with the second motion data,the processor is to: determine whether the first motion data satisfies athreshold; and determine whether the second motion data satisfies thethreshold.
 7. The apparatus of claim 2, further including a wirelessreceiver to receive the motion data reported by the one or more of thefirst accelerometer and the second accelerometer.
 8. The apparatus ofclaim 2, further including: a microphone to sense the output mediasignal from the monitored media device; a first tap to access the firstmedia signal from the first source device; and a second tap to accessthe second media signal from the second source device.
 9. Anon-transitory computer readable medium comprising computer readableinstructions that, when executed, cause a processor to at least:determine that an output media signal from a monitored media devicecorresponds to (i) a first media signal from a first source device incommunication with the monitored media device, and (ii) a second mediasignal from a second source device in communication with the monitoredmedia device; access motion data reported by one or more of a pluralityof accelerometers including a first accelerometer associated with afirst remote control device and a second accelerometer associated with asecond remote control device, the first remote control device associatedwith the first source device, the second remote control deviceassociated with the second source device; and identify, based on themotion data, one of the first source device or the second source deviceto be a source of the output media signal from the monitored mediadevice.
 10. The non-transitory computer readable medium of claim 9,wherein the instructions, when executed, cause the processor to:correlate the output media signal with the first media signal todetermine a correlation result; and determine the output media signalcorresponds to the first media signal when the correlation resultsatisfies a threshold.
 11. The non-transitory computer readable mediumof claim 9, wherein the first source device is in communication with afirst media input of the monitored media device, the second sourcedevice is in communication with a second media input of the monitoredmedia device, the motion data includes first motion data reported by thefirst remote control device and second motion data reported by thesecond remote control device, and the instructions, when executed, causethe processor to compare the first motion data and the second motiondata to determine whether the first source device or the second sourcedevice is the source of the output media signal.
 12. The non-transitorycomputer readable medium of claim 11, wherein the instructions, whenexecuted, cause the processor to: identify the first source device to bethe source of the output media signal when the first motion data is morerecent than the second motion data; and identify the second sourcedevice to be the source of the output media signal when the secondmotion data is more recent than the first motion data.
 13. Thenon-transitory computer readable medium of claim 11, wherein to comparethe first motion data with the second motion data, the instructions,when executed, cause the processor to: determine whether the firstmotion data satisfies a threshold; and determine whether the secondmotion data satisfies the threshold.
 14. The non-transitory computerreadable medium of claim 9, wherein the instruction, when executed,cause the processor to: access the motion data from a wireless receiver;access the output media signal from a microphone; access the first mediasignal from a first tap; and access the second media signal from asecond tap.
 15. A method to perform media source detection, the methodcomprising: determining that an output media signal from a monitoredmedia device corresponds to (i) a first media signal from a first sourcedevice in communication with the monitored media device, and (ii) asecond media signal from a second source device in communication withthe monitored media device; accessing motion data reported by one ormore of a plurality of accelerometers including a first accelerometerassociated with a first remote control device and a second accelerometerassociated with a second remote control device, the first remote controldevice associated with the first source device, the second remotecontrol device associated with the second source device; andidentifying, based on the motion data and by executing an instructionwith a processor, one of the first source device or the second sourcedevice to be a source of the output media signal from the monitoredmedia device.
 16. The method of claim 15, wherein the determining thatthe output media signal corresponds to the first media signal includes:correlating the output media signal with the first media signal todetermine a correlation result; and determining the output media signalcorresponds to the first media signal when the correlation resultsatisfies a threshold.
 17. The method of claim 15, wherein the firstsource device is in communication with a first media input of themonitored media device, the second source device is in communicationwith a second media input of the monitored media device, the motion dataincludes first motion data reported by the first remote control deviceand second motion data reported by the second remote control device, andthe identifying includes comparing the first motion data and the secondmotion data to determine whether the first source device or the secondsource device is the source of the output media signal.
 18. The methodof claim 17, wherein the comparing includes: identifying the firstsource device to be the source of the output media signal when the firstmotion data is more recent than the second motion data; and identifyingthe second source device to be the source of the output media signalwhen the second motion data is more recent than the first motion data.19. The method of claim 17, wherein the comparing includes: determiningwhether the first motion data satisfies a threshold; and determiningwhether the second motion data satisfies the threshold.
 20. The methodof claim 15, wherein the first accelerometer is a first wirelessaccelerometer tag affixed to the first remote control device, the secondaccelerometer is a second wireless accelerometer tag affixed to thesecond remote control device, and further including receiving the motiondata wirelessly from the first wireless accelerometer tag and the secondwireless accelerometer tag.
 21. The method of claim 15, furtherincluding: sensing the output media signal with a microphone; accessingthe first media signal with a first tap; and accessing the second mediasignal with a second tap.